•Firms increase risk disclosure when investor sentiment is higher.•The results are robust to the inclusion of both firm-level and macro-level control variables.•Our findings remain significant when ...we use alternative proxies for risk disclosure and investor sentiment.•This phenomenon is stronger for firms with higher risks, and is weaker for firms with greater concerns over price declines.
We show that investor sentiment in the stock market significantly increases public firms’ risk disclosure. The effect is more pronounced for firms with greater risks, and is weaker for firms with greater concerns over stock price declines.
Sentiment analysis is a task of natural language processing that has recently attracted increasing attention. However, sentiment analysis research has mainly been carried out for the English ...language. Although Arabic is ramping up as one of the most used languages on the Internet, only a few studies have focused on Arabic sentiment analysis so far. In this paper, we carry out an in-depth qualitative study of the most important research works in this context by discussing strengths and limitations of existing approaches. In particular, we survey both approaches that leverage machine translation or transfer learning to adapt English resources to Arabic and approaches that stem directly from the Arabic language.
•Arabic sentiment analysis is challenging due to complex morphology and dialects.•This paper provides an in-depth review of sentiment analysis research in Arabic.•We outline the limitations of current sentiment resources and methods for Arabic.•Most sentiment analysis approaches fail in Arabic social media space due to dialects.•We propose to shift from word-level to concept-based sentiment analysis.
The inception and rapid growth of the Web, social media, and other online forums have resulted in the continuous and rapid generation of opinionated textual data. Several real-world applications have ...been focusing on determining the sentiments expressed in these data. Owing to the multilinguistic nature of the generated data, there exists an increasing need to perform sentiment analysis on data in diverse languages. This study presents an overview of the methods used to perform sentiment analysis across languages. We primarily focus on multilingual and cross-lingual approaches. This survey covers the early approaches and current advancements that employ machine learning and deep learning models. We categorize these methods and techniques and provide new research directions. Our findings reveal that deep learning techniques have been widely used in both approaches and yield the best results. Additionally, the scarcity of multilingual annotated datasets limits the progress of multilingual and cross-lingual sentiment analyses, and therefore increases the complexity in comparing these techniques and determining the ones with the best performance.
Recently sentiment analysis in Arabic has attracted much attention from researchers. A modest number of studies have been conducted on Arabic sentiment analysis. However, due to the vast increase in ...users' comments and reviews on social media and e-commerce websites, the necessity to detect sentence-level and aspect-level sentiments has also increased. The aspect-based sentiment analysis has emerged to detect sentiments at the aspect level. Few studies have attempted to perform aspect-based sentiment analysis on Arabic texts because Arabic natural language processing is a challenging task and because of the lack of available Arabic annotated corpora. In this paper, we conducted a systematic review of the methods, techniques, and datasets employed in aspect-based sentiment analysis on Arabic texts. A total of 21 articles published between 2015-2021 were included in this review. After analysing these articles, we found a lack of annotated datasets that can be used by researchers. In addition, the used datasets were limited to few fields. This review will serve as a foundation for researchers interested in Aspect-Based Sentiment Analysis, it will assist them in developing new models and techniques to tackle this task in the future.
Sentiment strength detection is an essential task in sentiment analysis, wherein the sentiment strength of subjective text is automatically determined. Sentiment analysis has numerous applications in ...different sectors, including business and social domains. In this study, we present a model to effectively extract the features and strength of sentiment from words and text using a context-dependent, lexicon-based convolutional neural network. To build this convolutional neural network, the model is trained using the sentiment polarity for each word from a co-occurrence pattern of words and labels. Then, a context-dependent lexicon is generated from the corpus, which is used to generate positive and negative sentiment word embeddings. Positive sentiment word embeddings, negative sentiment word embeddings, and the pre-trained word embeddings are input to a 3-channel convolutional neural network (CNN) to predict the strength of the sentiments. Moreover, with the trained convolutional neural network model, we can obtain a learned sentiment strength-specific word embedding, which generates a sentiment strength-specific lexicon (SSS-Lex) that contains word associations and sentiment intensity scores. To validate the effectiveness of sentiment strength detection in the proposed model, we evaluate the model using six real-world datasets. Furthermore, to evaluate the sentiment strength-specific lexicon, we compare it with seven existing lexicons in three evaluation tasks from the SemEval-2015 and SemEval-2016 competitions. Experimental results indicate that the proposed model can predict the sentiment strength of documents more effectively than the baseline methods, and that the SSS-Lex is of higher quality than the existing lexicons.
Email data has unique characteristics, involving multiple topics, lengthy replies, formal language, high variance in length, high duplication, anomalies, and indirect relationships that distinguish ...it from other social media data. In order to better model Email documents and to capture complex sentiment structures in the content, we develop a framework for document-level multi-topic sentiment classification of Email data. Note that, a large volume of labeled Email data is rarely publicly available. We introduce an optional data augmentation process to increase the size of datasets with synthetically labeled data to reduce the probability of overfitting and underfitting during the training process. To generate segments with topic embeddings and topic weighting vectors as inputs for our proposed model, we apply both latent Dirichlet allocation topic modeling and semantic text segmentation to post-process Email documents. Empirical results obtained with multiple sets of experiments, including performance comparison against various state-of-the-art algorithms with and without data augmentation and diverse parameter settings, are analyzed to demonstrate the effectiveness of our proposed framework.
Sentiment Analysis (SA) is an ongoing field of research in text mining field. SA is the computational treatment of opinions, sentiments and subjectivity of text. This survey paper tackles a ...comprehensive overview of the last update in this field. Many recently proposed algorithms' enhancements and various SA applications are investigated and presented briefly in this survey. These articles are categorized according to their contributions in the various SA techniques. The related fields to SA (transfer learning, emotion detection, and building resources) that attracted researchers recently are discussed. The main target of this survey is to give nearly full image of SA techniques and the related fields with brief details. The main contributions of this paper include the sophisticated categorizations of a large number of recent articles and the illustration of the recent trend of research in the sentiment analysis and its related areas.
Aspect-based sentiment analysis is a fine-grained sentiment analysis task, which needs to detection the sentiment polarity towards a given aspect. Recently, graph neural models over the dependency ...tree are widely applied for aspect-based sentiment analysis. Most existing works, however, they generally focus on learning the dependency information from contextual words to aspect words based on the dependency tree of the sentence, which lacks the exploitation of contextual affective knowledge with regard to the specific aspect. In this paper, we propose a graph convolutional network based on SenticNet to leverage the affective dependencies of the sentence according to the specific aspect, called Sentic GCN. To be specific, we explore a novel solution to construct the graph neural networks via integrating the affective knowledge from SenticNet to enhance the dependency graphs of sentences. Based on it, both the dependencies of contextual words and aspect words and the affective information between opinion words and the aspect are considered by the novel affective enhanced graph model. Experimental results on multiple public benchmark datasets illustrate that our proposed model can beat state-of-the-art methods.
Sentiment analysis is an important natural language processing (NLP) task due to a wide range of applications. Most existing sentiment analysis techniques are limited to the analysis carried out at ...the aggregate level, merely providing negative, neutral and positive sentiments. The latest deep learning-based methods have been leveraged to provide more than three sentiment classes. However, such learning-based methods are still black-box-based methods rather than explainable language processing methods. To address this gap, this paper proposes a new explainable fine-grained multi-class sentiment analysis method, namely MiMuSA, which mimics the human language understanding processes. The proposed method involves a multi-level modular structure designed to mimic human’s language understanding processes, e.g., ambivalence handling process, sentiment strength handling process, etc. Specifically, multiple knowledge bases including Basic Knowledge Base, Negation and Special Knowledge Base, Sarcasm Rule and Adversative Knowledge Base, and Sentiment Strength Knowledge Base are built to support the sentiment understanding process. Compared with other multi-class sentiment analysis methods, this method not only identifies positive or negative sentiments, but can also understand fine-grained multi-class sentiments, such as the degree of positivity (e.g., strongly positive or slightly positive) and the degree of negativity (e.g., slightly negative or strongly negative) of the sentiments involved. The experimental results demonstrate that the proposed MiMuSA outperforms other existing multi-class sentiment analysis methods in terms of accuracy and F1-Score.
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